Intoxication from marijuana (MJ) impairs psychomotor performance and at least doubles the risk of motor vehicle accidents. The ongoing wave of legalization of MJ has brought increasing prevalence of driving while intoxicated with MJ. However, there is no quantitative biologic test that can accurately determine whether an individual is acutely impaired from MJ intoxication. Assays of the primary intoxicating substance in MJ, THC, in body fluids has a high false negative rate as THC is cleared from blood within 15 minutes, long before impairment is resolved. And assays of THC metabolites yield a high false positive rate because clearance of these metabolites can take weeks. Thus there is now no nor is there likely to ever be a test of blood, breath or body fluids that can accurately detect MJ intoxication. In response to this significant knowledge gap, this project aims to develop an accurate, portable method for detection of impairment due to MJ intoxication using functional near-infrared spectroscopy (fNIRS). fNIRS is a non-invasive, safe brain imaging technique that capitalizes on differences in the light absorption spectra of deoxygenated and oxygenated hemoglobin (Hb), that allows the measurement of relative changes in Hb concentration that reflect brain activity. fNIRS can be performed in natural environments at low cost, and thus can be used in real-world settings. In Phase I, we will develop an algorithm for individual-level detection of impairment from THC using fNIRS measurements. To do so, we will assess the effect of oral THC (or placebo) on fNIRS measurements, self-reported intoxication, and impairment as defined by the gold standard field sobriety test conducted by a Drug Recognition Expert (DRE) in 40 healthy MJ users. fNIRS assessments will examine (1) the effect of THC exposure on resting state and task-based activation in the prefrontal cortex, (2) the extent to which impairment in psychomotor functioning with THC administration correlates with THC-induced change in hemodynamic responses detected with fNIRS, and (3) the sensitivity and specificity and area under the ROC curve of fNIRS measurements and field sobriety test determinants of impairment. Milestone: Should machine learning applications to the data generate an algorithm that predicts impairment with >80% accuracy compared with a gold standard field sobriety test, we will proceed to Phase II. In Phase II, we will conduct fNIRS testing in 150 individuals under THC/placebo as in Phase I and in 50 individuals in a THC plus alcohol/placebo condition in order to further refine the algorithm for MJ impairment detection such that fNIRS detection concurs with field sobriety testing with >90% specificity. It is anticipated that this level of specificity could be used in legal definitions of impairment. This will warrant commercialization, which will be followed by prototype development and field testing. An accurate, quantitative, biological test that is user-friendly and enables law enforcement to detect impairment from MJ has the potential to dramatically change practice of law enforcement across the country and the world and thus has enormous commercial potential, as outlined in the Commercialization Plan and in accompanying letters of support.